This repository has various open sources tools which can be utilized in order to perform geospatial analysis. There are interactive Jupyter Notebooks available for demonstration purposes via Binder. Developers can also install Python packages and run the code on their own in Spyder. For GIS experts, GeoDa and QGIS installation links are included at the bottom of the repo.
- Getting Started
- Motivation and Objectives
- Background
- Introduction to GIS
- Introduction to DEVS
- Introduction to Spatial Analysis
- How do GIS and DEVS relate to each other
- How will spatial analysis be used to build this relationship between GIS and DEVS
- Model Generation Workflows
- Discussion
- Scenarios
- Credits and Acknowledgements
- Resources
- Appendix
Select the scenario_notebooks folder once Binder has loaded the repo in Jupyter Notebook. Next, choose any notebook to demo. Make sure to set the kernel to Python 3 when prompted to do so.
Note: Some cells may require more than a few seconds or minutes to run. Some libraries may also be unsupported by Binder.
-
In a console, cd into your desired directory and run the following:
git clone https://github.com/omarkawach/spatial_analysis_scenarios.git -
Open a console where you have cloned the repo, install all necessary python packages in one go by using:
pip install -r packages.txt -
You may now run/manipulate code
Modelling and Simulation (M&S) has shown to be useful for studying real-world systems and supporting decision-making through models that abstract systems under study. Building accurate models that adequately represent real-world systems can be both difficult and time consuming, especially on a large-scale for complex spatial systems such as emergency response / services, urban logistics, etc. Since GIS data contains highly detailed, often hierarchically organized information that can be used to build simulation models, it is compatible with the modular nature of DEVS formalism. With this information in mind, the goal of this research is to determine a method for automating the generation of large-scale, spatial DEVS simulation models from GIS data.
Below is from my presentation...
The first step is to determine how GIS data can be used to build complex, large-scale spatial DEVS models.
This work seeks to answer several questions for future, more extensive research on the topic:
- Which type of scenarios can GIS data help us model?
- Which spatial analysis tools can be used? How can they be used?
- What is a typical workflow that can convert GIS data into a simulation model?
- How can DEVS models be parameterized from GIS data?
- How can neighborhoods or couplings be derived from spatial properties?
Geographic information systems (GIS) open the door to the analysis, manipulation, and visualization of data spatially referenced to Earth. The two main components of spatial data are location (where) and attributes (what). These components of spatial data are mappable digitally and / or on paper. There also exist two main types of data in GIS. Vector data (objects) and raster data (field). Vector data can be 0-dimensional, 1-dimensional, and 2-dimensional. In the 0th dimension, coordinate points exist on their own. In the 1st dimension, two points can be used to create a line. In the 2nd dimension, three or more lines can be joined to make a polygon. Rasters can either be continuous (progressive data that varies) or discrete (thematic or categorical). Rectangular tessellated rasters are most common since they are easier mathematically.
Maybe discuss the importance of a coordinate system?
-- Bruno --
The utilization of spatial analysis techniques in the field of GIS is imperative for revealing patterns, especially when solving real-world problems. From the wide range of spatial analysis techniques, this paper will focus on topological processing, spatial/attribute querying, network analysis, and interpolation techniques.
How to perform spatial analysis - ESRI
Topology describes the relationship between one or more features within a space. In geometry, topological data does not consider factors like adjacency, contiguity, connectivity, intersection, etc. On the other hand, GIS requires topological data to be far more intelligent. Using topology to understand spatial relationships is an integral part of GIS.
When conducting GIS research, an area of interest needs to be selected. Buffer (proximity) analysis is the first step one could take to do this. Depending on the coordinate system being used, a buffer distance can be in degrees or other units of measurement like meters, kilometers, feet, miles, etc. The application of buffer analysis does not stop at creating study areas. Buffer analysis could also be used for counting the number of spatial features within a radius.
For Buffer Analysis: Make sure to change to a projected coordinate system before using buffers. For Ottawa, use UTM Zone 18N WGS 84. If we do not change the coordinate system, the buffer tool will assume degrees instead of distance.
Whether one is using polygons, lines, or points, various spatial relationship types could be used when conducting vector data analysis. Touches, contains, intersects, relation, within, crosses, and overlaps are some of the common keywords one would come across in a typical GIS application like QGIS. When using these relationship types, new layers are created. This process is called topological overlaying. For example, when the input is a point layer and the overlay layer is a polygon, then the output is a point. Therefore, the output of a topological overlay operation is always based on the input. The output can have attributes transferred by the input/output into the new layer or have no attributes transferred at all.
For an automated approach to proximity analysis in topology, one could take a collection of Voronoi (Thiessen) polygons and divide them by their distance to a point. Voronoi Methods in GIS
Spatial networks
Spatial Join (analysis)
- Attribute queries are...to be cont'd
- spatial queries are...
- Filtering
- https://www.researchgate.net/publication/321376749_Spatial_and_Attribute_Querying
Isochrones for service areas??
Might be better to do this in ArcGIS: SERVICE AREAS TO SHOW TIME INTERVALS OF TRAVEL
Could also try Iso-Area as Polygons (from layer) but the projection should be in UTM zones instead: https://root676.github.io/
Network analysis is commonly used in instances of urban planning / logistics studies.
Least cost path versus Origin-Destination (OD) matrix
Sometimes shortest path isn't better than fastest path algorithm. Depends on whether you want to minimize distance travelled or minimize time travelled.
Consider km/hr, typically 50km/hr by default.
- IDW (Inverse Distance Weighting, a local method)
- Follows TFL / Spatial autocorrelation
- Close things are more related than distant things
- Might not be good for surface calculations like elevation, but should be sufficient for our case since we'll look at concentration of elevation points.
- Follows TFL / Spatial autocorrelation
"IDW and NN methods have been found to be good for interpolation of geo-morphologically smooth areas" "Kriging methods take into consideration autocorrelation structures of elevations in order to define optimal weights." A comparative analysis of different DEM interpolation methods
Below is from my presentation...
Inverse Distance Weighting with Nearest Neighbor
- Averages interpolated points
- Prefers continuous concentration of points
- Would work better for mineral extraction models
Triangulated Irregular Network
- Requires more work but is good for surface calculations (elevation data)
- Produces a large file since it is computationally expensive
Below is from my presentation... Before modelling, the following questions must be answered:
- Is there a real-world system to model a spatial system?
- Are there any spatial relationships that can be used?
- Is geospatial data available?
- Data is easy to find, good, complete data? not so much
- Can the data be geo-processed?
- Are the right tools available? Is the required hardware available?
If all questions are answered, then we can consider a model generation workflow:
The rise in population across developed countries continues to put a strain on ambulance services and health care systems. Unlike ambulance services, studies that address the strain health care systems face are a lot more well-documented and reported on in the media (Lowthian et al., 2011). For example, initiatives by the New South Wales (NSW) Ambulance to reduce ambulance demand has been proven to be ineffective due to the lack of reliable and consistent information. Only recently did NSW Ambulance begin publicly reporting on emergency ambulance response time (NSW Govt, 2017). Unfortunately, when it comes to patient outcome, response time data does not work well as a performance metric. Patient outcome can be improved through proper triage (prioritize) and dispatching, ambulance deployment modelling, and new technologies and processes like GIS (Al-Shaqsi, 2010). Through modern GIS software or shortest path algorithm, dispatchers can dispatch and relocate ambulances while considering travel time and location (Nguyen, 2015)...
In the era of COVID-19, a protocol for patient at-home testing by trained paramedics could be brought to use (Glauser, 2020). Hypothetically, resources for such a protocol should be allocated based on the proximity of a patient's residence to a hospital. Figure 4 depicts a study area composed of 3 Ottawa dissemination areas (DA) and the buildings within them. A graphical modeler can be utilised to automate the workflow of calculating the shortest path between a health care facility and a patient's residence (see Figure 5). Afterwards, buildings can be assigned to their nearest hospital by using a simple python script. We'll call this assignment "Health Unit-Building (HUB) coupling". Then, all these HUB couplings will produce an Emergency Service Coupled Model (see Figure 6)... Come up with resulting model, any house that emits a call
Figure 4. Three Ottawa DAs and their Buildings
Figure 5. Road Distance Graphical Modeler
Figure 6. Health Unit - Building Couplings. Map created using QGIS 3.14 (https://www.qgis.org/en/site/).
Figure 7. Emergency Service Model Generation Workflow (needs to be updated, see presentation)
As an extension to this would be to target patient treatment by hospital speciality as well (El-Masri, and Saddik, 2012). Can also consider population and number of buildings to service.
Obviously, it (avoid expletives) would not be feasible to only cater to 3 DAs, so one must consider how to allocate emergency services on a city-wide scale. This is where more topological processing and network analysis continue to come in handy. From each DA polygon, a centroid can be extracted. Then, QNEAT3's OD Matrix in QGIS is used to find the network cost for an ambulance to go from a DA to a hospital. An output containing the distance from each DA to each hospital will appear. A simple python script is then used to assign each DA to their closest hospital.
Process to move OD Matrix results to DA shapefile QNEAT3 Distance Matrices "OD Matrix from layers as lines (m:n)". Fix CRS first Join Attributes by Field Value to DA choose ones within 2.5km using select by expression tool.
- Match DAUID to origin_id
- Keep destination ID and total cost
- Join type is one-to-many
- Discard records which could not be joined
- Set prefix to OD_
- Discard records that couldn't be joined
Figure 7. Ottawa DAs Mapped to their Nearest Hospital. Map created using QGIS 3.14 (https://www.qgis.org/en/site/).
Alternate approach below (remove if not necessary)
Another option is to use Network analysis tool service area from layer and then convex hull or concave hull with NN
Limitations:
- The accuracy is left up to QGIS
- We end up not serving houses that are on the same road
- We might have to expand the width ourselves
Why is it so difficult to find papers on the growth of platform-to-customer delivery through apps?.
Companies that currently run prescription delivery generally lack economic moat. A Canadian pharmacy chain, such as Shoppers Drug Mart, already has established customers and buying power for new technologies. If they were to release a prescription delivery application, they would likely dominate the market. To save on costs and increase efficiencies, Shoppers Drug Mart could deliver prescriptions to customers from the closest pharmacy. Hypothetically, before launching the app, Shoppers Drug Mart would first need to find willing participants to test a beta version of their prescription delivery application.
In Ottawa, Jonathan, a 24-year-old Carleton University student has recently tested positive for COVID-19 and must self-isolate for two weeks. He calls his local Shoppers Drug Mart to see if they can deliver his monthly prescribed medication. The pharmacist at Jonathan's local Shoppers Drug Mart store informed him that they don't currently offer such services, but it is in their services pipeline. To avoid waiting for prescription delivery services to be available, the pharmacist asks Jonathan if he'd like to participate in beta testing their new prescription delivery application. Jonathan agrees and provides Shoppers Drug Mart with his consent to conduct the research.
Before creating a study area, Tim, a GIS Analyst working for Shoppers Drug Mart must first ask himself the following questions:
- Where is Jonathan located?
- What distance(s) are delivery drivers permitted to drive?
- Where are the Shoppers Drug Marts within the driveable distance?
To answer these questions, Tim will need to acquire current data specific to Ottawa such as road networks, building footprints, and Shoppers Drug Mart locations. Once this data has been acquired, only then can he start extracting data to fit the extent (or match the scope) of his study area. Using building footprints, Tim locates Jonathan's building, and sets a 2.5km buffer around it. Tim uses a spatial intersection tool to find that 3 local Shoppers Drug Marts are within this buffer. Then, he uses Ottawa's road network to find the shortest path between Jonathan's building and the 3 local Shoppers Drug Marts. With some basic SQL code, only the closest Shopper's Drug Mart to Jonathan's building is coupled. In this case, a delivery driver would be dispatched from the Merivale location to deliver Jonathan's prescribed medication (see Figure 8).
The delivery service workflow generated for this model can be reincorporated to not only support prescription delivery for pharmacies, but also to link distribution centers to population centers, warehouses to stores, warehouses to stores to customers, etc. For the case of pharmacies, the Graphical Modeler in Figure 9 automates the majority of the delivery service workflow. The final output would be the path to the closest pharmacy, which is what was seen in Figure 8.
Going a step further, the workflow can be implemented on a much larger scale. Suppose Shoppers is ready to launch their application and would like to have every Shoppers Drug Mart deliver prescriptions to customers within a 2.5km distance buffer. Voronoi diagrams would allow analysts to identify the closest Shoppers to a customer's building. This can be done through creating Voronoi polygons from all the Shoppers Drug Mart point data with a large buffer extent and then clipping the Voronoi polygons with our specified 2.5km distance buffer (see Figure 13). Then we can use the Graphical Modeler as seen in Figure 11 to find the shortest path between the closest Shoppers to a customer.
Figure 8. Closest Pharmacy
Figure 9. Closest Pharmacy Graphical Modeler
Figure 10. Delivery Service Model Generation Workflow (needs to be updated, see presentation)
Figure 11. Graphical Modeler for Finding the Distance between Pharmacies and Customers
Figure 12. Buildings with Access to Prescription Delivery near McCarthy
Figure 13. Large Scale Delivery
Figure 14. Delivery Service Coupled Model (change this entirely)
In Indonesia's Palu City, proactive disaster response, recovery, and relief are essential during emergency situations. Gadjah Mada University researchers implemented multi-ring buffers to capture shelter accessibility for Palu City. The researchers also considered road networks for mobility to access the shelters in the event of an earthquake. (Fatma et al., 2019). The application of multi-ring buffers does not stop at earthquake preparedness studies. Since there are many emergency situations to consider, this paper will focus on floods, chemical spills, and power outages.
As severe flooding increases across Canada due to climate change Burton, 2015, proactive measures by various levels of government are required. Without such intervention, flooding in regions like Ottawa-Gatineau will continue to become a problem for infrastructure (McNeil, 2019). Sandbags are commonly used as a defense against floods. Having the data to protect homes and neighbors would be vital information. For example, assume a 1km flood risk buffer was created in one neighbourhood and then split into quarters via the multi-ring buffer method. Each ring in Figure 18 can represent a sandbag line of defense so first responders can allot sandbags accordingly. The limitation for this model is that it does not take elevation into consideration. The speed and height at which water approaches a home is an important factor. The DAs surrounding the Wastewater Treatment Plant in Ottawa has a diverse amount of elevation. The severity of the flooding has been mapped using a three-ring buffer. Each ring in the three-ring buffer will represent 600m for a total of 1.8km. Also, the homes at the highest risk are those under 70m of elevation. To specify the amount of elevation in an area, Inverse Distance Weighting (IDW) with Nearest Neighbour (NN) Analysis is used to build a raster. Then we use the raster calculator to find which areas are below 70m of elevation. We then polygonise the raster and intersect it with the three-ring buffer.
Figure 15. Elevation Contours
Figure 16. IDW with NN
Figure 17. Raster Calculator Result (or add distance weight)
Figure 18. Buildings at Risk by Danger Zones and Elevations. Map created using QGIS 3.14 (https://www.qgis.org/en/site/).
Following the Lac-Mégantic rail disaster, the Transport Safety Board (TSB) of Canada made a few recommendations to be better prepared to prevent similar disasters. One of the recommendations was to have "Emergency response assistance plans must be created when large volumes of liquid hydrocarbons, like oil, are shipped" (TSB, 2019). Help dispatch and evacuate people...
Ottawa's Montfort Hospital encounters a chemical spill. Residents reside within 0.5km of the hospital are warned to evacuate immediately. Residents whose ONS Boundary touch with the 1.5km buffer are also expected to evacuated moments later. Before First Responders head into the polygons that are impacted by the chemical spill, they want to know the number of buildings that are impacted in order to prioritize their action or their response. This research would allow First Responders to know an approximate headcount as well.
Figure 19. Overview of Chemical Spill Scenario. Map created using QGIS 3.14 (https://www.qgis.org/en/site/).
https://www.usbr.gov/power/edu/pamphlet.pdf
Hydroelectricity from the Hydro Ottawa Rideau Falls facility is capable of delivering power to consumers. Should an outage occur, a large number of buildings within ONS polygons that touch the 2km power outage buffer would be out of power. If such an event were to occur, the City of Ottawa would like to know which buildings are without power and which ONS polygon they reside in.
Used 'Extract by location' instead of intersect to find polygons touching buffer
Figure 20. Ottawa DAs and Buildings Without Power. Map created using QGIS 3.14 (https://www.qgis.org/en/site/).
Ottawa resident, Mary, is looking for a new apartment to rent. She tells her Real Estate Agent that she would like to live within a boundary that has a hospital in it since she requires regular visits for post-operation checkups. To meet Mary's apartment criteria, a spatial join (analysis) must be conducted.
GIS analysts conduct spatial joins by taking one feature and seeing if it intersects with another feature. In this case, the GIS analyst would see if a hospital point is within an ONS polygon. This would allow us to define new neighbourhoods based on whether a polygon has a hospital or not. For this spatial join, we could also try one or more spatial relationship types like touches, contains, within, and relation.
After analysis, Mary would receive results stating that she may look for apartments in the ten following areas:
- Civic Hospital
- Central Park
- Billings Bridge
- Alta Vista
- Riverview
- Wateridge Village
- Qualicum
- Redwood Park
- West Centretown
- Byward Market
Figure 1. ONS polygons with Hospitals in them
At Statistics Canada, an intern would like to conduct a population density study using ONS data. The data gathered would be used for defining new neighbourhoods based on a simple quartile classification algorithm.
Figure 2. ONS Population Density
Legend
Figure 3. ONS Quartile Classification for New Neighbourhoods
Potential Use Case
- Model highly infectious populations according to population density
- We may theorize that more dense areas play an impact on the number of cases in neighbouring polygons.
- Expand to have topological relationships
- Maybe having buffers based on infection spread
JTFS
Find the nearest bus stop to each building within specific Ottawa DAs. This can be done by using bus stops to create voronoi polygons. Then, intersect the voronoi polygons with the Ottawa DAs. This scenario assumes that the vertices from each bus route are bus stops, even if that is not the case.
Figure 4. Overview of Closest Bus Stop(s) to each Building in DAs
We're in the center of Ottawa and want to find the shortest path to a road crossing through network analysis.
Figure 5. Shortest path
For the ONS dataset, we are looking for ways to represent the spatial relationships between polygons. We do this by depicting a spatial weight network (planar). In this case, the Queen contiguity weight lets us look at shared edges or vertices between polygons.
Figure 6. Spatial Weight Network Ottawa
Potential Use Case
- Boundary Detection to observe differences in wealth (Spatial Econometrics)
Sarah Loos - UVic GEOG 328 GIS Analysis
Qiusheng Wu - Python Geospatial Repo on GitHub
Binder: Notebooks in an Executable Environment
Jupyter Notebook: Interactive Python Notebooks
Matplotlib: Visualization with Python
Pandas: Data Analysis in Python
GeoPandas: Work with Geospatial Data in Python
Shapely: Manipulate and Analyze Geometric Objects
NetworkX: Network Analysis in Python
OSMnx: Python for Street Networks
PyProj: For Projections in Geospatial Data
PySal: Spatial Analysis Library
NumPy: Scientific Computing with Python
Descartes: For Plotting Polygons in GeoPandas
Geovoronoi: For Plotting Voronoi Regions inside Geographic Regions
QGIS Download: Open Source Geospatial Analysis Program
QGIS Docs: Tutorials and Tips
Spyder: Scientific Python Development Environment
Ottawa Neighbourhood Study (ONS) from Carleton University
Shapefile Location
See /shapefiles/ONS
Description from source:
The ONS Neighbourhood Boundaries were created by the Ottawa Neighbourhood Study
(ONS) to analyse population statistics and are not indicative of actual
neighbourhood limits. Neighbourhood refers to an inhabited area delineated by
social and physical boundaries. ONS neighbourhood boundaries were derived based on
census tracts, physical and demographic similarities, physical barriers (e.g.
waterways, highways, etc.), maps used by the real estate profession (e.g. the
Ottawa Multiple Listing Service), consultations with community stakeholders, as
well as fieldwork by ONS researchers.
108 neighbourhood boundaries are included in the shapefile,
from Barrhaven to Woodvale.
Shapefile Location
See /shapefiles/OttawaHospitals
Description from source:
Map containing point features of Hospitals located in the City of Ottawa.
Open Database of Building from Statistics Canada
Shapefile Location
See /shapefiles/OttawaBuildings.zip
Description from source:
The Open Database of Buildings (ODB) is a collection of open data on buildings,
primarily building footprints, and is made available under the Open Government
License - Canada.
Ottawa Dissemination Areas (DA) from Open Ottawa
Shapefile Location
Description from source:
The Dissemination Area Boundary Files portray the dissemination area boundaries
for which Census data are disseminated. A dissemination area is a small area
composed of one or more neighbouring dissemination blocks and is the smallest
standard geographic area for which all census data are disseminated.
OC Transpo Routes from Carleton University
Shapefile Location
See /shapefiles/OC_TRANSPO_ROUTES
Modifications to Original Dataset in this Repo:
The dataset came with hundreds of shapefiles. All the shapefiles were merged into one.
Description from source:
Individual vector transit routes for OC Transpo between 1929 and 2015.
The following information was collected for each transit route:
- Route Number
- Route Type (Regular, Peak, Express, Rural, etc.)
- Mode of Transportation (bus, train)
- Year
Road Network Files from Statistics Canada
Shapefile Location
Modifications to Original Dataset in this Repo:
The dataset came with the whole road network of Canada. Modified the dataset to only have Ottawa's road network.
Description from source:
Road network files are digital representations of Canada's
national road network, containing information such as street
names, types, directions and address ranges.
Waterbody Data from Rideau Value Conservation Authority (RVCA)
Description from Source
https://rvcagis.github.io/jkan/datasets/rvca-waterbodies/
RVCA Waterbodies represent the Lakes, Ponds and large Rivers within the RVCA, as a
polygon feature class. They have been delineated using the MNRF LIO waterbody standard
and using the existing LIO Waterbody layer as a base. This dataset is used extensively
for Subwatershed & Catchment Reporting, as well as Regulations